Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations411804
Missing cells16887
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory31.4 MiB
Average record size in memory80.0 B

Variable types

DateTime1
Text1
Numeric8

Alerts

biweekly_cases is highly overall correlated with biweekly_deaths and 4 other fieldsHigh correlation
biweekly_deaths is highly overall correlated with biweekly_cases and 4 other fieldsHigh correlation
new_cases is highly overall correlated with new_deathsHigh correlation
new_deaths is highly overall correlated with new_casesHigh correlation
total_cases is highly overall correlated with biweekly_cases and 4 other fieldsHigh correlation
total_deaths is highly overall correlated with biweekly_cases and 4 other fieldsHigh correlation
weekly_cases is highly overall correlated with biweekly_cases and 4 other fieldsHigh correlation
weekly_deaths is highly overall correlated with biweekly_cases and 4 other fieldsHigh correlation
biweekly_cases has 4597 (1.1%) missing values Missing
biweekly_deaths has 4148 (1.0%) missing values Missing
new_cases is highly skewed (γ1 = 97.87907533) Skewed
new_deaths is highly skewed (γ1 = 36.2068314) Skewed
weekly_cases is highly skewed (γ1 = 37.1102558) Skewed
biweekly_cases is highly skewed (γ1 = 31.63448452) Skewed
new_cases has 368385 (89.5%) zeros Zeros
new_deaths has 382900 (93.0%) zeros Zeros
total_cases has 29304 (7.1%) zeros Zeros
total_deaths has 52361 (12.7%) zeros Zeros
weekly_cases has 116965 (28.4%) zeros Zeros
weekly_deaths has 215615 (52.4%) zeros Zeros
biweekly_cases has 102909 (25.0%) zeros Zeros
biweekly_deaths has 195390 (47.4%) zeros Zeros

Reproduction

Analysis started2025-06-06 12:09:04.158510
Analysis finished2025-06-06 12:09:29.906085
Duration25.75 seconds
Software versionydata-profiling v4.16.1
Download configurationconfig.json

Variables

date
Date

Distinct1674
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
Minimum2020-01-05 00:00:00
Maximum2024-08-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-06T14:09:30.250048image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:30.591012image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct246
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:31.511682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length9.5243902
Min length4

Characters and Unicode

Total characters3922182
Distinct characters58
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
and 16740
 
2.9%
islands 16740
 
2.9%
saint 11718
 
2.1%
countries 6696
 
1.2%
united 6696
 
1.2%
south 6696
 
1.2%
french 5022
 
0.9%
republic 5022
 
0.9%
guinea 5022
 
0.9%
new 5022
 
0.9%
Other values (277) 485460
85.0%
2025-06-06T14:09:32.659797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 547398
14.0%
i 333126
 
8.5%
n 324756
 
8.3%
e 276210
 
7.0%
r 232686
 
5.9%
o 214272
 
5.5%
t 169074
 
4.3%
159030
 
4.1%
u 155682
 
4.0%
s 143964
 
3.7%
Other values (48) 1365984
34.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3922182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 547398
14.0%
i 333126
 
8.5%
n 324756
 
8.3%
e 276210
 
7.0%
r 232686
 
5.9%
o 214272
 
5.5%
t 169074
 
4.3%
159030
 
4.1%
u 155682
 
4.0%
s 143964
 
3.7%
Other values (48) 1365984
34.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3922182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 547398
14.0%
i 333126
 
8.5%
n 324756
 
8.3%
e 276210
 
7.0%
r 232686
 
5.9%
o 214272
 
5.5%
t 169074
 
4.3%
159030
 
4.1%
u 155682
 
4.0%
s 143964
 
3.7%
Other values (48) 1365984
34.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3922182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 547398
14.0%
i 333126
 
8.5%
n 324756
 
8.3%
e 276210
 
7.0%
r 232686
 
5.9%
o 214272
 
5.5%
t 169074
 
4.3%
159030
 
4.1%
u 155682
 
4.0%
s 143964
 
3.7%
Other values (48) 1365984
34.8%

new_cases
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct14158
Distinct (%)3.5%
Missing1645
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean8017.3599
Minimum0
Maximum44236227
Zeros368385
Zeros (%)89.5%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:33.001531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile569
Maximum44236227
Range44236227
Interquartile range (IQR)0

Descriptive statistics

Standard deviation229664.87
Coefficient of variation (CV)28.645947
Kurtosis14084.785
Mean8017.3599
Median Absolute Deviation (MAD)0
Skewness97.879075
Sum3.2883923 × 109
Variance5.2745951 × 1010
MonotonicityNot monotonic
2025-06-06T14:09:33.329466image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 368385
89.5%
1 1074
 
0.3%
2 785
 
0.2%
3 629
 
0.2%
4 501
 
0.1%
5 432
 
0.1%
6 407
 
0.1%
7 339
 
0.1%
9 308
 
0.1%
8 296
 
0.1%
Other values (14148) 37003
 
9.0%
(Missing) 1645
 
0.4%
ValueCountFrequency (%)
0 368385
89.5%
1 1074
 
0.3%
2 785
 
0.2%
3 629
 
0.2%
4 501
 
0.1%
5 432
 
0.1%
6 407
 
0.1%
7 339
 
0.1%
8 296
 
0.1%
9 308
 
0.1%
ValueCountFrequency (%)
44236227 1
< 0.1%
42142424 1
< 0.1%
40975782 1
< 0.1%
40475477 1
< 0.1%
27732700 1
< 0.1%
26088587 1
< 0.1%
25061799 1
< 0.1%
24644876 1
< 0.1%
23541614 1
< 0.1%
23424524 1
< 0.1%

new_deaths
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct3500
Distinct (%)0.9%
Missing1196
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean71.852139
Minimum0
Maximum103719
Zeros382900
Zeros (%)93.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:33.680297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum103719
Range103719
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1368.323
Coefficient of variation (CV)19.043594
Kurtosis1697.8336
Mean71.852139
Median Absolute Deviation (MAD)0
Skewness36.206831
Sum29503063
Variance1872307.8
MonotonicityNot monotonic
2025-06-06T14:09:34.038847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 382900
93.0%
1 3136
 
0.8%
2 1839
 
0.4%
3 1212
 
0.3%
4 868
 
0.2%
5 721
 
0.2%
6 607
 
0.1%
7 520
 
0.1%
8 454
 
0.1%
9 435
 
0.1%
Other values (3490) 17916
 
4.4%
(Missing) 1196
 
0.3%
ValueCountFrequency (%)
0 382900
93.0%
1 3136
 
0.8%
2 1839
 
0.4%
3 1212
 
0.3%
4 868
 
0.2%
5 721
 
0.2%
6 607
 
0.1%
7 520
 
0.1%
8 454
 
0.1%
9 435
 
0.1%
ValueCountFrequency (%)
103719 1
< 0.1%
100970 1
< 0.1%
100661 1
< 0.1%
96003 1
< 0.1%
92351 1
< 0.1%
92233 1
< 0.1%
91431 1
< 0.1%
89292 1
< 0.1%
87600 1
< 0.1%
86299 1
< 0.1%

total_cases
Real number (ℝ)

High correlation  Zeros 

Distinct36694
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7365292.4
Minimum0
Maximum7.7586678 × 108
Zeros29304
Zeros (%)7.1%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:34.403303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16280.75
median63653
Q3758272
95-th percentile22898376
Maximum7.7586678 × 108
Range7.7586678 × 108
Interquartile range (IQR)751991.25

Descriptive statistics

Standard deviation44775817
Coefficient of variation (CV)6.0792993
Kurtosis154.77797
Mean7365292.4
Median Absolute Deviation (MAD)63653
Skewness11.235906
Sum3.0330569 × 1012
Variance2.0048738 × 1015
MonotonicityNot monotonic
2025-06-06T14:09:34.746031image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29304
 
7.1%
1 1680
 
0.4%
26 1590
 
0.4%
4 1499
 
0.4%
3 1001
 
0.2%
17786 897
 
0.2%
34658 883
 
0.2%
11971 862
 
0.2%
15690 785
 
0.2%
994037 764
 
0.2%
Other values (36684) 372539
90.5%
ValueCountFrequency (%)
0 29304
7.1%
1 1680
 
0.4%
2 567
 
0.1%
3 1001
 
0.2%
4 1499
 
0.4%
5 644
 
0.2%
6 329
 
0.1%
7 567
 
0.1%
8 448
 
0.1%
9 413
 
0.1%
ValueCountFrequency (%)
775866783 1
 
< 0.1%
775819614 7
< 0.1%
775763598 7
< 0.1%
775711519 7
< 0.1%
775664972 7
< 0.1%
775617900 7
< 0.1%
775571394 7
< 0.1%
775534258 7
< 0.1%
775498015 7
< 0.1%
775462248 7
< 0.1%

total_deaths
Real number (ℝ)

High correlation  Zeros 

Distinct16763
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81259.574
Minimum0
Maximum7057132
Zeros52361
Zeros (%)12.7%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:35.119456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q143
median799
Q39574
95-th percentile222666
Maximum7057132
Range7057132
Interquartile range (IQR)9531

Descriptive statistics

Standard deviation441190.14
Coefficient of variation (CV)5.4293927
Kurtosis132.33116
Mean81259.574
Median Absolute Deviation (MAD)799
Skewness10.269144
Sum3.3463018 × 1010
Variance1.9464874 × 1011
MonotonicityNot monotonic
2025-06-06T14:09:35.475948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52361
 
12.7%
1 6246
 
1.5%
2 3810
 
0.9%
21 2522
 
0.6%
12 2354
 
0.6%
125 2277
 
0.6%
3 2163
 
0.5%
7 1813
 
0.4%
9 1793
 
0.4%
8 1744
 
0.4%
Other values (16753) 334721
81.3%
ValueCountFrequency (%)
0 52361
12.7%
1 6246
 
1.5%
2 3810
 
0.9%
3 2163
 
0.5%
4 938
 
0.2%
5 1541
 
0.4%
6 1197
 
0.3%
7 1813
 
0.4%
8 1744
 
0.4%
9 1793
 
0.4%
ValueCountFrequency (%)
7057132 1
 
< 0.1%
7056317 7
< 0.1%
7055413 7
< 0.1%
7054581 7
< 0.1%
7053847 7
< 0.1%
7053137 7
< 0.1%
7052480 7
< 0.1%
7051782 7
< 0.1%
7051229 7
< 0.1%
7050714 7
< 0.1%

weekly_cases
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct14158
Distinct (%)3.5%
Missing2875
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean56287.18
Minimum0
Maximum44236227
Zeros116965
Zeros (%)28.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:35.841425image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median84
Q32193
95-th percentile105372.6
Maximum44236227
Range44236227
Interquartile range (IQR)2193

Descriptive statistics

Standard deviation606312.78
Coefficient of variation (CV)10.771774
Kurtosis2021.4194
Mean56287.18
Median Absolute Deviation (MAD)84
Skewness37.110256
Sum2.301746 × 1010
Variance3.6761519 × 1011
MonotonicityNot monotonic
2025-06-06T14:09:36.163764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116965
28.4%
1 7459
 
1.8%
2 5478
 
1.3%
3 4397
 
1.1%
4 3501
 
0.9%
5 3024
 
0.7%
6 2843
 
0.7%
7 2373
 
0.6%
9 2156
 
0.5%
8 2072
 
0.5%
Other values (14148) 258661
62.8%
(Missing) 2875
 
0.7%
ValueCountFrequency (%)
0 116965
28.4%
1 7459
 
1.8%
2 5478
 
1.3%
3 4397
 
1.1%
4 3501
 
0.9%
5 3024
 
0.7%
6 2843
 
0.7%
7 2373
 
0.6%
8 2072
 
0.5%
9 2156
 
0.5%
ValueCountFrequency (%)
44236227 7
< 0.1%
42142424 7
< 0.1%
40975782 7
< 0.1%
40475477 7
< 0.1%
27732700 7
< 0.1%
26088587 7
< 0.1%
25061799 7
< 0.1%
24644876 7
< 0.1%
23541614 7
< 0.1%
23424524 7
< 0.1%

weekly_deaths
Real number (ℝ)

High correlation  Zeros 

Distinct3500
Distinct (%)0.9%
Missing2426
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean504.426
Minimum0
Maximum103719
Zeros215615
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:36.475785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q322
95-th percentile1105
Maximum103719
Range103719
Interquartile range (IQR)22

Descriptive statistics

Standard deviation3595.456
Coefficient of variation (CV)7.1278166
Kurtosis240.84057
Mean504.426
Median Absolute Deviation (MAD)0
Skewness13.649219
Sum2.0650091 × 108
Variance12927304
MonotonicityNot monotonic
2025-06-06T14:09:36.849377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 215615
52.4%
1 21910
 
5.3%
2 12867
 
3.1%
3 8459
 
2.1%
4 6064
 
1.5%
5 5047
 
1.2%
6 4237
 
1.0%
7 3640
 
0.9%
8 3178
 
0.8%
9 3039
 
0.7%
Other values (3490) 125322
30.4%
ValueCountFrequency (%)
0 215615
52.4%
1 21910
 
5.3%
2 12867
 
3.1%
3 8459
 
2.1%
4 6064
 
1.5%
5 5047
 
1.2%
6 4237
 
1.0%
7 3640
 
0.9%
8 3178
 
0.8%
9 3039
 
0.7%
ValueCountFrequency (%)
103719 7
< 0.1%
100970 7
< 0.1%
100661 7
< 0.1%
96003 7
< 0.1%
92351 7
< 0.1%
92233 7
< 0.1%
91431 7
< 0.1%
89292 7
< 0.1%
87600 7
< 0.1%
86299 7
< 0.1%

biweekly_cases
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct16957
Distinct (%)4.2%
Missing4597
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean113044.3
Minimum0
Maximum71968927
Zeros102909
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:37.179328image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median183
Q34507
95-th percentile213772
Maximum71968927
Range71968927
Interquartile range (IQR)4507

Descriptive statistics

Standard deviation1167969.7
Coefficient of variation (CV)10.331965
Kurtosis1402.5566
Mean113044.3
Median Absolute Deviation (MAD)183
Skewness31.634485
Sum4.6032429 × 1010
Variance1.3641532 × 1012
MonotonicityNot monotonic
2025-06-06T14:09:37.509849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 102909
 
25.0%
1 5747
 
1.4%
2 4633
 
1.1%
3 3731
 
0.9%
4 2976
 
0.7%
5 2571
 
0.6%
6 2379
 
0.6%
7 2198
 
0.5%
8 1968
 
0.5%
10 1862
 
0.5%
Other values (16947) 276233
67.1%
(Missing) 4597
 
1.1%
ValueCountFrequency (%)
0 102909
25.0%
1 5747
 
1.4%
2 4633
 
1.1%
3 3731
 
0.9%
4 2976
 
0.7%
5 2571
 
0.6%
6 2379
 
0.6%
7 2198
 
0.5%
8 1968
 
0.5%
9 1814
 
0.4%
ValueCountFrequency (%)
71968927 7
< 0.1%
68231011 7
< 0.1%
66037581 7
< 0.1%
65120353 7
< 0.1%
59213741 7
< 0.1%
54823117 7
< 0.1%
52681477 7
< 0.1%
51574027 7
< 0.1%
46966138 7
< 0.1%
44203319 7
< 0.1%

biweekly_deaths
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct4452
Distinct (%)1.1%
Missing4148
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean1013.0469
Minimum0
Maximum204689
Zeros195390
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size3.1 MiB
2025-06-06T14:09:37.826345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q345
95-th percentile2244
Maximum204689
Range204689
Interquartile range (IQR)45

Descriptive statistics

Standard deviation7162.04
Coefficient of variation (CV)7.0698008
Kurtosis239.11342
Mean1013.0469
Median Absolute Deviation (MAD)1
Skewness13.568757
Sum4.1297466 × 108
Variance51294816
MonotonicityNot monotonic
2025-06-06T14:09:38.127557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 195390
47.4%
1 19794
 
4.8%
2 13190
 
3.2%
3 9088
 
2.2%
4 6605
 
1.6%
5 5258
 
1.3%
6 4508
 
1.1%
7 3745
 
0.9%
8 3075
 
0.7%
10 2593
 
0.6%
Other values (4442) 144410
35.1%
(Missing) 4148
 
1.0%
ValueCountFrequency (%)
0 195390
47.4%
1 19794
 
4.8%
2 13190
 
3.2%
3 9088
 
2.2%
4 6605
 
1.6%
5 5258
 
1.3%
6 4508
 
1.1%
7 3745
 
0.9%
8 3075
 
0.7%
9 2590
 
0.6%
ValueCountFrequency (%)
204689 7
< 0.1%
204380 7
< 0.1%
193321 7
< 0.1%
189953 7
< 0.1%
188236 7
< 0.1%
187434 7
< 0.1%
179833 7
< 0.1%
178650 7
< 0.1%
177341 7
< 0.1%
173789 7
< 0.1%

Interactions

2025-06-06T14:09:21.841699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:09.357701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:10.959054image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:12.693046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:14.397731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:16.207643image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:17.785140image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:19.257092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:22.223624image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:09.512872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:11.135492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:12.931421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:14.613050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:16.417276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:17.959418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:19.440303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:22.677947image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:09.676642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:11.365741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:13.151209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:14.823649image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:16.635398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:18.139484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:19.625344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:23.136677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:09.916168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:11.599484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:13.379487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:15.011340image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:16.853248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:18.336476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:19.832912image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:24.172455image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:10.105690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:11.830344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:13.637822image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:15.374074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:17.076891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:18.513407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:20.061738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:24.663591image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:10.276382image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:11.989977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:13.850891image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:15.572537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:17.247972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:18.692359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:20.549223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:25.378326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:10.491248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:12.179877image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:14.027477image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:15.769772image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:17.421298image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:18.876910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:21.057536image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:25.789784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:10.750398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:12.500976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:14.204072image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:15.999565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:17.600062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:19.073592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-06-06T14:09:21.473021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-06-06T14:09:38.326781image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
biweekly_casesbiweekly_deathsnew_casesnew_deathstotal_casestotal_deathsweekly_casesweekly_deaths
biweekly_cases1.0000.8880.1770.2320.5940.6110.9860.867
biweekly_deaths0.8881.0000.1390.2620.5480.6080.8820.971
new_cases0.1770.1391.0000.8200.0880.0940.1830.135
new_deaths0.2320.2620.8201.0000.1350.1510.2330.271
total_cases0.5940.5480.0880.1351.0000.9610.5880.543
total_deaths0.6110.6080.0940.1510.9611.0000.6060.600
weekly_cases0.9860.8820.1830.2330.5880.6061.0000.869
weekly_deaths0.8670.9710.1350.2710.5430.6000.8691.000

Missing values

2025-06-06T14:09:26.203888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-06T14:09:26.999344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-06T14:09:29.175116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datelocationnew_casesnew_deathstotal_casestotal_deathsweekly_casesweekly_deathsbiweekly_casesbiweekly_deaths
02020-01-05Afghanistan0.00.000NaNNaNNaNNaN
12020-01-06Afghanistan0.00.000NaNNaNNaNNaN
22020-01-07Afghanistan0.00.000NaNNaNNaNNaN
32020-01-08Afghanistan0.00.000NaNNaNNaNNaN
42020-01-09Afghanistan0.00.000NaNNaNNaNNaN
52020-01-10Afghanistan0.00.0000.00.0NaNNaN
62020-01-11Afghanistan0.00.0000.00.0NaNNaN
72020-01-12Afghanistan0.00.0000.00.0NaNNaN
82020-01-13Afghanistan0.00.0000.00.0NaNNaN
92020-01-14Afghanistan0.00.0000.00.0NaNNaN
datelocationnew_casesnew_deathstotal_casestotal_deathsweekly_casesweekly_deathsbiweekly_casesbiweekly_deaths
4117942024-07-26Zimbabwe0.00.026638657401.00.01.00.0
4117952024-07-27Zimbabwe0.00.026638657401.00.01.00.0
4117962024-07-28Zimbabwe0.00.026638657400.00.01.00.0
4117972024-07-29Zimbabwe0.00.026638657400.00.01.00.0
4117982024-07-30Zimbabwe0.00.026638657400.00.01.00.0
4117992024-07-31Zimbabwe0.00.026638657400.00.01.00.0
4118002024-08-01Zimbabwe0.00.026638657400.00.01.00.0
4118012024-08-02Zimbabwe0.00.026638657400.00.01.00.0
4118022024-08-03Zimbabwe0.00.026638657400.00.01.00.0
4118032024-08-04Zimbabwe0.00.026638657400.00.00.00.0